Functional near infrared spectroscopy (fNIRS) is an emerging non-invasive imaging technique to assess the brain function. The technique is non-invasive and portable and therefore applicable in studies of children and toddlers especially those with neurodevelopmental disorders. fNIRS measurements are based on the local changes in cerebral hemodynamic levels (oxy-hemoglobin and deoxy-hemoglobin) associated with brain activity. Due to the low optical absorption of biological tissues at NIR wavelengths (700-900 nm), NIR light can penetrate deep enough to probe the cortical regions up to 1-3 cm deep. As mentioned above, the NIR absorption spectrum of the tissue is sensitive to changes in the concentration of major tissue chromophores, such as hemoglobin. Therefore, measurements of temporal variations of backscattered light can capture functionally evoked changes in the outermost cortex and can be used to assess the brain function. However, there is a need to address the changes in NIRS signal in relation with underlying physiological processes in brain such as cerebral autoregulation. In short, the mechanism of cerebral autoregulation maintains the blood flow over the range of arterial blood pressure and due to high metabolic demand of neurons it becomes a vital process for a brain function. Devising a novel method of data processing to enrich informational content of measured characteristics from fNIRS is therefore crucial for further studies of brain function and development. In this pilot study, we evaluated the utility of functional near infrared spectroscopy (fNIRS) in measuring cerebral hemodynamics in the prefrontal cortex (PFC) in toddlers between 18 and 36 months of age. Further, we analyzed group differences in fNIRS data in toddlers with typical development and a group of children at risk for developmental during a vanilla baseline period. The analysis includes assessment of the hemodynamic activation, bases on changes in oxy-hemoglobin, in left and right prefrontal cortex, and the oxygenation variability (OV) index based on variability in oxygen saturation at frequencies attributed to cerebral autoregulation for each child. In our pilot study we found that AR participants showed preliminary evidence for both more right-sided activation (2 (1)=4.2, p=0.04, =0.38) and higher differences between left and right activation (2 (1)=12, p=0.001, =0.64) compared to the TD group. In addition, AR toddlers had a significant lower overall OV index compared to children with typical development in both the left (2 (1)=9.45, p=.002, =0.57) and right PFC (2 (1)=9.15, p=.002, =0.56). It is worth mentioning that the OV index is not a direct measure of cerebral autoregulation. Rather, it is associated with frequencies related to this mechanism and serves to quantify oscillations in those frequencies. While this study suggests that some features of prefrontal hemodynamics may vary in toddlers at risk for developmental delays due to early language delay, more research in this age group is required to clarify the specificity of these differences. These preliminary findings show the feasibility of using fNIRS in typical toddlers and those with delayed development and in doing so support future studies in larger samples. We have explored the potential prefrontal hemodynamic biomarkers to characterize subjects with Traumatic Brain Injury (TBI) by employing a multivariate machine learning approach and introducing a novel task-related hemodynamic response detection followed by a heuristic search for optimum set of hemodynamic features. To achieve this goal, the hemodynamic response from a group of 31 healthy controls and 30 chronic TBI subjects were recorded as they performed a complexity task. To determine the optimum hemodynamic features, we considered 11 features and their combinations in characterizing TBI subjects. We investigated the significance of the features by utilizing a machine learning classification algorithm to score all the possible combinations of features according to their predictive power. The identified optimum feature elements resulted in classification accuracy, sensitivity, and specificity of 85%, 85%, and 84%, respectively. Classification improvement was achieved for TBI subject classification through feature combination. It signified the major advantage of the multivariate analysis over the commonly used univariate analysis suggesting that the features that are individually irrelevant in characterizing the data may become relevant when used in combination. We also conducted a spatio-temporal classification to identify regions within the prefrontal cortex (PFC) that contribute in distinguishing between TBI and healthy subjects. As expected, Brodmann areas (BA) 10 within the PFC were isolated as the region that healthy subjects (unlike subjects with TBI), showed major hemodynamic activity in response to the High Complexity task. Overall, our results indicate that identified temporal and spatio-temporal features from PFC's hemodynamic activity are promising biomarkers in classifying subjects with TBI.